Bounded output is an AI or automation result limited to a known set of answers, such as allow, deny, escalate, or a small list of labels. Instead of free-form text, the system must choose from predefined options. This makes machine action easier to control because downstream software can parse the result reliably and map it to a specific workflow.
In cyber security, bounded output matters because it is easier to audit, threshold, and log than open-ended language. Security tools use it for ticket routing, content classification, risk scoring, and escalation logic. The main risk is over-trusting the model: a wrong label, low-confidence decision, or manipulated input can trigger the wrong automated action. Good defenses add confidence thresholds, fallback paths to human review, and strict schemas so the model can only return permitted choices.